Gibbs
Robert and Casella (2013) gives the following algorithm, while Liu, Jun S. (2008) introduces two types of Gibbs sampling strategy. # Bivariate Gibbs sampler It is easy to implement this sampler:
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## Julia program for Bivariate Gibbs sampler
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## author: weiya <[email protected]>
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## date: 2018-08-22
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function bigibbs(T, rho)
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x = ones(T+1)
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y = ones(T+1)
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for t = 1:T
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x[t+1] = randn() * sqrt(1-rho^2) + rho*y[t]
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y[t+1] = randn() * sqrt(1-rho^2) + rho*x[t+1]
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end
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return x, y
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end
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## example
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bigibbs(100, 0.5)
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# Completion Gibbs Sampler Example: We can use the following Julia program to implement this algorithm.
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## Julia program for Truncated normal distribution
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## author: weiya <[email protected]>
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## date: 2018-08-22
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# Truncated normal distribution
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function rtrunormal(T, mu, sigma, mu_down)
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x = ones(T)
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z = ones(T+1)
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# set initial value of z
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z = rand()
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if mu < mu_down
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z = z * exp(-0.5 * (mu - mu_down)^2 / sigma^2)
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end
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for t = 1:T
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x[t] = rand() * (mu - mu_down + sqrt(-2*sigma^2*log(z[t]))) + mu_down
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z[t+1] = rand() * exp(-(x[t] - mu)^2/(2*sigma^2))
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end
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return(x)
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end
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## example
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rtrunormal(1000, 1.0, 1.0, 1.2)
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# Slice Sampler

Robert and Casella (2013) introduces the following slice sampler algorithm, and Liu, Jun S. (2008) also presents the slice sampler with slightly different expression: In my opinion, we can illustrate this algorithm with one dimensioanl case. Suppose we want to sample from normal distribution (or uniform distribution), we can sample uniformly from the region encolsed by the coordinate axis and the density function, that is a bell shape (or a square).
Consider the normal distribution as an instance. It is also easy to write the following Julia program.
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## Julia program for Slice sampler
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## author: weiya <[email protected]>
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## date: 2018-08-22
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function rnorm_slice(T)
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x = ones(T+1)
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w = ones(T+1)
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for t = 1:T
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w[t+1] = rand() * exp(-1.0 * x[t]^2/2)
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x[t+1] = rand() * 2 * sqrt(-2*log(w[t+1])) - sqrt(-2*log(w[t+1]))
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end
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return x[2:end]
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end
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## example
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rnorm_slice(100)
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# Data Augmentation

A special case of Completion Gibbs Sampler. Let's illustrate the scheme with grouped counting data. And we can obtain the following algorithm, But it seems to be not obvious to derive the above algorithm, so I wrote some more details Liu, Jun S. (2008) also presents the DA algorithm which based on Bayesian missing data problem. Then he argues that
$m$
copies of
$\mathbf y_{mis}$
in each iteration is not really necessary. And briefly summary the DA algorithm:  It seems to agree with the algorithm presented by Robert and Casella (2013).
Another example: It seems that we do not need to derive the explicit form of
$g(x, z)$
, if we can directly obtain the conditional distribution. We can use the following Julia program to sample.
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## Julia program for Grouped Multinomial Data (Ex. 7.2.3)
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## author: weiya <[email protected]>
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## date: 2018-08-26
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# call gamma function
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#using SpecialFunctions
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# sample from Dirichlet distributions
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using Distributions
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function gmulti(T, x, a, b, alpha1 = 0.5, alpha2 = 0.5, alpha3 = 0.5)
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z = ones(T+1, size(x, 1)-1) # initial z satisfy z <= x
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mu = ones(T+1)
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eta = ones(T+1)
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for t = 1:T
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# sample from g_1(theta | y)
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dir = Dirichlet([z[t, 1] + z[t, 2] + alpha1, z[t, 3] + z[t, 4] + alpha2, x + alpha3])
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sample = rand(dir, 1)
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mu[t+1] = sample
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eta[t+1] = sample
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# sample from g_2(z | x, theta)
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for i = 1:2
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bi = Binomial(x[i], a[i]*mu[t+1]/(a[i]*mu[t+1]+b[i]))
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z[t+1, i] = rand(bi, 1)
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end
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for i = 3:4
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bi = Binomial(x[i], a[i]*eta[t+1]/(a[i]*eta[t+1]+b[i]))
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z[t+1, i] = rand(bi, 1)
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end
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end
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return mu, eta
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end
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# example
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## data
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a = [0.06, 0.14, 0.11, 0.09];
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b = [0.17, 0.24, 0.19, 0.20];
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x = [9, 15, 12, 7, 8];
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gmulti(100, x, a, b)
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# Reversible Data Augmentation # Reversible Gibbs Sampler # Random Sweep Gibbs Sampler # Random Gibbs Sampler # Hybrid Gibbs Samplers # Metropolization of the Gibbs Sampler Let us illuatrate this algorithm with the following example.  